Text prediction using environment hints

ABSTRACT

Provided are techniques for text prediction using environment hints. A list of words is received, wherein each word in the list of words has an associated weight. For at least one word in the list of words, an environment weight is obtained from an environment dictionary. The associated weight of the at least one word is updated using the obtained environment weight. The words in the list of words are ordered based on the updated, associated weight of each of the words.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application is a continuation of U.S. patent applicationSer. No. 13/682,594, filed on Nov. 20, 2012, which patent application isincorporated herein by reference in its entirety.

FIELD

Embodiments of the invention relate to text prediction using environmenthints.

BACKGROUND

Auto-completion of words is a feature seen in contexts such as textmessaging, word processing, web forms, cloud applications, etc. Suchauto-completion takes into account the context of a word in a sentence.The auto-completion receives a portion of a word (e.g., one or moreletters), and provides auto-completion suggestions. For example, if auser types in “mont”, then the auto-completion suggestions may include“month”.

SUMMARY

Provided are a method, computer program product, and system for textprediction using environment hints. A list of words is received, whereineach word in the list of words has an associated weight. For at leastone word in the list of words, an environment weight is obtained from anenvironment dictionary. The associated weight of the at least one wordis updated using the obtained environment weight. The words in the listof words are ordered based on the updated, associated weight of each ofthe words.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Referring now to the drawings in which like reference numbers representcorresponding parts throughout:

FIG. 1 illustrates a computing environment in accordance with certainembodiments.

FIG. 2 illustrates, in a flow diagram, operations for providingauto-completion suggestions in accordance with certain embodiments. FIG.2 is formed by FIG. 2A and FIG. 2B.

FIG. 3 illustrates, in a flow diagram, operations for creating one ormore environment dictionaries in accordance with certain embodiments.

FIG. 4 depicts a cloud computing node in accordance with certainembodiments.

FIG. 5 depicts a cloud computing environment in accordance with certainembodiments.

FIG. 6 depicts abstraction model layers in accordance with certainembodiments.

DETAILED DESCRIPTION

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

FIG. 1 illustrates a computing environment in accordance with certainembodiments. A server computer 100 is coupled to a computing device 150(e.g., a client computer, a tablet computer, a personal digitalassistant (PDA), portable device, etc.). The server computer 100includes an auto-completion process 120 and one or more environmentdictionaries 130. In certain embodiments, such as a cloud environment,the server computer 100 includes a prediction engine 170. Theenvironment dictionaries 130 include information about the environmentthat may be used as hints in predicting completion of a word that a useris typing.

The computing device 150 includes a text input control 160 and mayinclude a prediction engine 170 and one or more environment dictionaries130. For example, in a cloud environment, the prediction engine 170 andthe one or more environment dictionaries 130 may reside on just theserver computer 100.

The user enters text (e.g., one or more characters of a word) into thetext input control 160, and, while the user is inputting the text, theprediction engine 170 uses the one or more environment dictionaries 130to provide auto-completion suggestions that the user may select from tocomplete the text.

In certain embodiments, the environment dictionaries 130 may be pushedfrom the server computer 100 to one or more computing devices (such ascomputing device 150) as updates occur. In certain alternativeembodiments, the environment dictionaries 130 may be pulled by the oneor more computing devices from the server computer 100 as needed orperiodically.

The prediction engine 170 takes into account a broader sense of physicalcontext based prediction by looking at the environment (e.g., currentweather conditions, location, date, user's activities, etc.). Usingenvironment hints enables the prediction engine 170 to more accuratelypredict the words that are most relevant to a user at a given time. Thisis especially useful on small devices where typing is difficult for someusers.

The prediction engine 170 introduces the idea of environment based hintsfor auto completion of words. An environment hint may take many forms,such as time of year, Global Positioning System (GPS) coordinates,altitude, velocity air temperature, humidity, barometric pressure,ambient light, etc. In certain embodiments, the environment hints areobtained from sensors collecting data. In certain embodiments, a user orother individual or application may provide the environment hints. Theseenvironment hints may be used in combination to determine manyattributes of what a user is doing/viewing at the current moment on acomputer or portable device, what words may be more relevant in thecurrent environment, and what the user's level of expertise is.Additionally, facial recognition software may be used to evaluate theuser's mood/environment information to further enhance the textprediction. Moreover, the prediction engine 170 may learn aboutenvironment hints in the case of a distributed system, such as webforms, cloud applications, etc., by observing word usage trends as theycorrelate to the environment cues.

The prediction engine 170 adds an additional factor to theauto-completion processing. In certain embodiments, once theauto-completion process 120 chooses words expected to complete a portionof a word (i.e., letters entered so far) that the user has input, theprediction engine 170 adds a new factor in the weighting of the selectedwords. The additional factor is an aggregation of environment values.

In certain embodiments, an environment dictionary 130 is provided foreach environment factor (e.g., time, location, weather, etc.). Eachenvironment dictionary 130 includes words that are related to theenvironment factor. For example, the time dictionary may include: 1)seasonal words such as Autumn and foliage; 2) holidays such Christmasand Halloween, and 3) words related to smaller scopes of time such asthe days of the week.

Each environment dictionary 130 includes an environment weighting foreach word. For example, the words Autumn and foliage may be morecommonly used words during the Fall season; while the words Christmas,shopping, and decorating may be more commonly used words as the holidayseason approaches.

Also, the same word may be included in multiple environment dictionaries130. For example, the words snow, ice, and blizzard may be used duringwinter months and may be included in the time dictionary 130 asassociated with a winter season. In addition, the words snow, ice, andblizzard may describe weather around the approach or passing of a winterstorm, which would be measured via air temperature, humidity, andbarometric pressure, and may be included in a weather dictionary 130.

In certain embodiments, the environment dictionaries 130 may be staticand remain unchanged. In certain other embodiments, the environmentdictionaries 130 may be dynamically generated. For example, as a user islooking at a document and writing an email on the side related to thatdocument, the prediction engine 170 generates the environmentdictionaries 130 for the document that the user is looking at anddynamically feeds the environment dictionaries 130 into the predictionof the word for auto-completion.

For a given word in the auto-complete list, the prediction engine 170searches each environment dictionary 130 and obtains an overallenvironment weight that can be based on one or more factors in theenvironment dictionaries 130. The environment weight is factored intothe weight obtained by the auto-completion process 120. After theenvironment weight is factored in, the prediction engine 170 generates afinal ordering of the words and presents these in the auto-completionlist to the user.

The environment weight may be described as a value based on externalfactors affecting people or based on a set of external conditions (e.g.,those affecting a particular activity), such as current weatherconditions, location, date, user's activities, etc.

The prediction engine 170 may use a pre-defined library of environmenthints, as well as automatically learn new hints in the case of adistributed environment (e.g., web forms, cloud applications, etc).

In particular, the prediction engine 170 provides environment hints forauto completion of words and uses the environment hints in combinationwith each other to determine attributes of what a user is doing/viewingat the current moment on the computing device 100. Also, the predictionengine 170 learns about new environment hints (e.g., in the case of adistributed system such as web forms, cloud applications, etc.) byobserving word usage trends as they correlate to the environment cues.

Merely to enhance understanding of embodiments, examples are providedherein.

In a first example, in the case of “time of year” hints in the fourthquarter of the calendar year, a text input of “Chr” may result in aprediction of “Christmas”, whereas in the second quarter, the same textinput may result in a prediction of “Chris”. However, the user may belocated (e.g., determined with GPS coordinates) in a country that doesnot celebrate this holiday, so the “Christmas” prediction may not beused in that environment. The more hints that can be correlated, themore accurate the prediction becomes.

In a second example, Lin is switching between reading web pages (e.g.,that were identified from a search from an internet search engine) anddrafting a document using a document tracking database. Based on the webpages she viewed most recently, the prediction engine 170 builds acollection of environment dictionaries 130 that contain words, phasesthat are of interest to her, etc. As she continues drafting herdocument, these words in the environment dictionaries 130 are given ahigher weight compared with other words (i.e., not in the environmentdictionaries 130) when providing a suggestion for auto-completion of aword. For example, if she looked at web pages related to a WebConference and Instant Messaging, whenever she types “Ins”, theprediction engine 170 may suggest “Instant” at the top of the list ofauto-completion suggestions.

FIG. 2 illustrates, in a flow diagram, operations for providingauto-completion suggestions in accordance with certain embodiments. FIG.2 is formed by FIG. 2A and FIG. 2B.

In FIG. 2A, control begins at block 200 with the prediction engine 170receiving a list of words from the auto-completion process 120, whereeach word in the list has an associated initial weight. In block 202,the prediction engine 170 selects a next word in the list, starting witha first word in the list. In block 204, the prediction engine 170locates the selected word and an associated environment weight in one ormore environment dictionaries 130. In certain embodiments, theprediction engine 170 searches for the selected word in everyenvironment dictionary 130. In alternative embodiments, the predictionengine 170 searches for the selected word in a subset (e.g., one ormore) of the environment dictionaries 130. In certain embodiments, thesubset of environment dictionaries are used based on the environment ofthe user (e.g., if the user is reading text messages, a subset ofdictionaries known to be related to text messages may be selected).

In block 206, the prediction engine 170 generates a total weight for theselected word by adding the initial weight and each environment weightfrom the one or more environment dictionaries in which the selected wordis located.

In block 208, the prediction engine 170 determines whether all the wordsin the list have been selected. If so, processing continues to block,otherwise, processing continues to block 210 (FIG. 2B).

In block 210, the prediction engine 170 orders the words in the listbased on the total weight of each word. In certain embodiments, thewords are ordered by highest total weight to lowest total weight. Incertain embodiments, if multiple words have the same total weight, theprediction engine 170 orders the words based on other factors (e.g.,most recently used item or length of the words).

In block 212, the prediction engine 170 presents the ordered words to auser as suggestions for auto-completion of a portion of a word alreadyinput by the user.

In certain embodiments, the prediction engine 170 works in the contextof a user having one or more social networks. A social network may bedescribed as a group of two or more users using computing devices tocommunicate with each other.

FIG. 3 illustrates, in a flow diagram, operations for creating one ormore environment dictionaries using one or more social networks inaccordance with certain embodiments. In FIG. 3, control begins in block300 with the prediction engine 170 collecting information about a user'senvironment. For example, the prediction engine 170 collects informationabout user actions (e.g., opening applications, reviewing documents,sending text messages, etc.) and the user's location as they occur.

In block 302, the prediction engine 170 collects information from one ormore social networks associated with the user. For example, theprediction engine 170 may collect information about what other users ina social network are reviewing, where the other users are, with whom theuser is texting, etc.

In block 304, the prediction engine 170 updates one or more environmentdictionaries 130 using the collected information about at least one ofthe user's environment and one or more social networks associated withthe user. In certain embodiments, the updating includes generating newenvironment dictionaries 130 and adding information to the newenvironment dictionaries 130. Thus, the prediction engine 170 buildscustom environment dictionaries 130 (e.g., general and friend specific)based on the locations, actions, and trends of the user and other usesin the social network.

Form block 304, processing loops back to block 300. Thus, the predictionengine 170 may be constantly updating the environment dictionaries 130such that they account for prior history and are influenced by thecurrent events of the user and other users in the social network.

Cloud Environment

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting for loadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 4, a schematic of an example of a cloud computingnode is shown. Cloud computing node 410 is only one example of asuitable cloud computing node and is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of theinvention described herein. Regardless, cloud computing node 410 iscapable of being implemented and/or performing any of the functionalityset forth hereinabove.

In cloud computing node 410 there is a computer system/server 412, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 412 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 412 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 412 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 4, computer system/server 412 in cloud computing node410 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 412 may include, but are notlimited to, one or more processors or processing units 416, a systemmemory 428, and a bus 418 that couples various system componentsincluding system memory 428 to a processor or processing unit 416.

Bus 418 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system/server 412 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 412, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 428 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 430 and/or cachememory 432. Computer system/server 412 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 434 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 418 by one or more datamedia interfaces. As will be further depicted and described below,memory 428 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 440, having a set (at least one) of program modules 442,may be stored in memory 428 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 442 generally carry out the functionsand/or methodologies of embodiments of the invention as describedherein.

Computer system/server 412 may also communicate with one or moreexternal devices 414 such as a keyboard, a pointing device, a display424, etc.; one or more devices that enable a user to interact withcomputer system/server 412; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 412 to communicate withone or more other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 422. Still yet, computer system/server 412can communicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 420. As depicted, network adapter 420communicates with the other components of computer system/server 412 viabus 418. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 412. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 5, illustrative cloud computing environment 550 isdepicted. As shown, cloud computing environment 550 comprises one ormore cloud computing nodes 410 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 554A, desktop computer 554B, laptop computer554C, and/or automobile computer system 554N may communicate. Nodes 410may communicate with one another. They may be grouped (not shown)physically or virtually, in one or more networks, such as Private,Community, Public, or Hybrid clouds as described hereinabove, or acombination thereof. This allows cloud computing environment 550 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 554A-Nshown in FIG. 5 are intended to be illustrative only and that computingnodes 410 and cloud computing environment 550 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers providedby cloud computing environment 550 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 660 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM® zSeries® systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM pSeries® systems; IBMxSeries® systems; IBM BladeCenter® systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM WebSphere®application server software; and database software, in one example IBMDB2® database software. (IBM, zSeries, pSeries, xSeries, BladeCenter,WebSphere, and DB2 are trademarks of International Business MachinesCorporation registered in many jurisdictions worldwide).

Virtualization layer 662 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 664 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 666 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and prediction of auto-completion suggestions.

Thus, in certain embodiments, software or a program, implementingprediction of auto-completion suggestions in accordance with embodimentsdescribed herein, is provided as a service in a cloud environment.

In certain embodiments, the server computer 100 and/or the computingdevice 160 has the architecture of computing node 410. In certainembodiments, the server computer 100 and/or the computing device 160 ispart of a cloud environment. In certain alternative embodiments, theserver computer 100 and/or the computing device 160 is not part of acloud environment. In embodiments in which the server computer 100 ispart of the cloud embodiment, the prediction engine 170 may execute onthe server computer 100 to provide a service of predictingauto-completion suggestions.

Additional Embodiment Details

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, solid state memory, magnetic tape orany suitable combination of the foregoing. In the context of thisdocument, a computer readable storage medium may be any tangible mediumthat can contain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the embodiments of the invention are described below withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems) and computer program products according toembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational processing (e.g., operations or steps) to beperformed on the computer, other programmable apparatus or other devicesto produce a computer implemented process such that the instructionswhich execute on the computer or other programmable apparatus provideprocesses for implementing the functions/acts specified in the flowchartand/or block diagram block or blocks.

The code implementing the described operations may further beimplemented in hardware logic or circuitry (e.g., an integrated circuitchip, Programmable Gate Array (PGA), Application Specific IntegratedCircuit (ASIC), etc. The hardware logic may be coupled to a processor toperform operations.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or moreintermediaries.

A description of an embodiment with several components in communicationwith each other does not imply that all such components are required. Onthe contrary a variety of optional components are described toillustrate the wide variety of possible embodiments of the presentinvention.

Further, although process steps, method steps, algorithms or the likemay be described in a sequential order, such processes, methods andalgorithms may be configured to work in alternate orders. In otherwords, any sequence or order of steps that may be described does notnecessarily indicate a requirement that the steps be performed in thatorder. The steps of processes described herein may be performed in anyorder practical. Further, some steps may be performed simultaneously.

When a single device or article is described herein, it will be readilyapparent that more than one device/article (whether or not theycooperate) may be used in place of a single device/article. Similarly,where more than one device or article is described herein (whether ornot they cooperate), it will be readily apparent that a singledevice/article may be used in place of the more than one device orarticle or a different number of devices/articles may be used instead ofthe shown number of devices or programs. The functionality and/or thefeatures of a device may be alternatively embodied by one or more otherdevices which are not explicitly described as having suchfunctionality/features. Thus, other embodiments of the present inventionneed not include the device itself.

The illustrated operations of the flow diagrams show certain eventsoccurring in a certain order. In alternative embodiments, certainoperations may be performed in a different order, modified or removed.Moreover, operations may be added to the above described logic and stillconform to the described embodiments. Further, operations describedherein may occur sequentially or certain operations may be processed inparallel. Yet further, operations may be performed by a singleprocessing unit or by distributed processing units.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The terms “an embodiment”, “embodiment”, “embodiments”, “theembodiment”, “the embodiments”, “one or more embodiments”, “someembodiments”, and “one embodiment” mean “one or more (but not all)embodiments of the present invention(s)” unless expressly specifiedotherwise.

The terms “including”, “comprising”, “having” and variations thereofmean “including but not limited to”, unless expressly specifiedotherwise.

The enumerated listing of items does not imply that any or all of theitems are mutually exclusive, unless expressly specified otherwise.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of embodiments of the present invention has been presentedfor purposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiments were chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The foregoing description of embodiments of the invention has beenpresented for the purposes of illustration and description. It is notintended to be exhaustive or to limit the embodiments to the preciseform disclosed. Many modifications and variations are possible in lightof the above teaching. It is intended that the scope of the embodimentsbe limited not by this detailed description, but rather by the claimsappended hereto. The above specification, examples and data provide acomplete description of the manufacture and use of the composition ofthe embodiments. Since many embodiments may be made without departingfrom the spirit and scope of the invention, the embodiments reside inthe claims hereinafter appended or any subsequently-filed claims, andtheir equivalents.

1. A method, comprising: receiving a list of words, wherein each word inthe list of words has an associated weight; for at least one word in thelist of words, obtaining an environment weight from an environmentdictionary; updating the associated weight of the at least one wordusing the obtained environment weight; and ordering the words in thelist of words based on the updated, associated weight of each of thewords.
 2. The method of claim 1, further comprising: collectinginformation about an environment of a user; and generating theenvironment dictionary based on the collected information.
 3. The methodof claim 1, further comprising: collecting information about one or moresocial networks associated with a user; and generating the environmentdictionary based on the collected information.
 4. The method of claim 1,further comprising: updating the environment dictionary based oninformation collected about at least one of an environment of the userand one or more social networks associated with the user.
 5. The methodof claim 1, further comprising: presenting the ordered words to a useras suggestions for completion of a portion of a word input by the user.6. The method of claim 1, further comprising: receiving the list ofwords from an auto-completion process that generates the list of wordsas suggestions for completion of a portion of a word input by the user.7. The method of claim 1, wherein a Software as a Service (SaaS) isprovided to perform the method.